Air Quality Model Evaluation International Initiative (AQMEII): Advancing the State of the Science in Regional Photochemical Modeling and Its Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In early 1980's, American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) held two workshops to discuss and recommend methods for evaluating plume dispersion models (Fox, 1981 and 1984). AMS and EPA also held another workshop in 1984 to discuss evaluation issues relating to regional-scale air quality models, but the workshop participants did not recommend any specific methods for the model performance evaluation (Demerjian, 1985). Hence, the statistical metrics identified by the first AMS and EPA workshop continue to be used for evaluating Gaussian dispersion models as well as numerical regional-scale air quality models, not only in the United States but also in other countries. Although the focus in 1970¿s and 1980¿s was primarily on urban air pollution models, it is well-known that pollution problems such as acid rain, ozone, and fine particulate matter are regional in scope, requiring regional-scale multi-pollutant models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it